{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "5d037743",
      "metadata": {
        "id": "5d037743"
      },
      "source": [
        "# Data Visualisation\n",
        "\n",
        "In this tutorial we'll visualise the signals in a MIMIC Waveform record.\n",
        "\n",
        "Our **objectives** are to:\n",
        "- Plot one minute of signals from a segment of data\n",
        "- Look more closely at the shape of the PPG pulse waves"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1afcdef9",
      "metadata": {
        "id": "1afcdef9"
      },
      "source": [
        "---\n",
        "## Setup\n",
        "\n",
        "<div class=\"alert alert-block alert-warning\">\n",
        "<p><b>Resource:</b> These steps are taken from the <a href=\"https://wfdb.io/mimic_wfdb_tutorials/tutorial/notebooks/data-exploration.html\">Data Exploration</a> tutorial.</p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "edd8e0c5",
      "metadata": {
        "id": "edd8e0c5"
      },
      "source": [
        "- Specify the required Python packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ce3cdfde",
      "metadata": {
        "id": "ce3cdfde"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "from pathlib import Path"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "61a223f7",
      "metadata": {
        "id": "61a223f7"
      },
      "source": [
        "- Install and import the WFDB toolbox"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "ee8532b2",
      "metadata": {
        "id": "ee8532b2",
        "outputId": "4b07988f-c1bc-4d06-ca99-32b21e9609f2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting wfdb==4.0.0\n",
            "  Downloading wfdb-4.0.0-py3-none-any.whl (161 kB)\n",
            "\u001b[K     |████████████████████████████████| 161 kB 20.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests<3.0.0,>=2.8.1 in /usr/local/lib/python3.7/dist-packages (from wfdb==4.0.0) (2.23.0)\n",
            "Requirement already satisfied: pandas<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wfdb==4.0.0) (1.3.5)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.10.1 in /usr/local/lib/python3.7/dist-packages (from wfdb==4.0.0) (1.21.6)\n",
            "Requirement already satisfied: scipy<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wfdb==4.0.0) (1.4.1)\n",
            "Requirement already satisfied: matplotlib<4.0.0,>=3.2.2 in /usr/local/lib/python3.7/dist-packages (from wfdb==4.0.0) (3.2.2)\n",
            "Requirement already satisfied: SoundFile<0.12.0,>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from wfdb==4.0.0) (0.10.3.post1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (1.4.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0.0,>=1.0.0->wfdb==4.0.0) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (1.15.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.8.1->wfdb==4.0.0) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.8.1->wfdb==4.0.0) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.8.1->wfdb==4.0.0) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.8.1->wfdb==4.0.0) (3.0.4)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.7/dist-packages (from SoundFile<0.12.0,>=0.10.0->wfdb==4.0.0) (1.15.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0->SoundFile<0.12.0,>=0.10.0->wfdb==4.0.0) (2.21)\n",
            "Installing collected packages: wfdb\n",
            "Successfully installed wfdb-4.0.0\n"
          ]
        }
      ],
      "source": [
        "!pip install wfdb==4.0.0\n",
        "import wfdb"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "460c706a",
      "metadata": {
        "id": "460c706a"
      },
      "source": [
        "- Specify the settings for the MIMIC-IV database"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "3fcd245f",
      "metadata": {
        "id": "3fcd245f"
      },
      "outputs": [],
      "source": [
        "# The name of the MIMIC IV Waveform Database on PhysioNet\n",
        "database_name = 'mimic4wdb/0.1.0'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "61a9432c",
      "metadata": {
        "id": "61a9432c"
      },
      "source": [
        "- Provide a list of segments which meet the requirements for the study (NB: these are copied from the end of the [Data Exploration Tutorial](https://wfdb.io/mimic_wfdb_tutorials/tutorial/notebooks/data-exploration.html))."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "bb5745a7",
      "metadata": {
        "id": "bb5745a7"
      },
      "outputs": [],
      "source": [
        "segment_names = ['83404654_0005', '82924339_0007']\n",
        "segment_dirs = ['mimic4wdb/0.1.0/waves/p100/p10020306/83404654',\n",
        "                'mimic4wdb/0.1.0/waves/p101/p10126957/82924339']"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8ed55718",
      "metadata": {
        "id": "8ed55718"
      },
      "source": [
        "- Specify a segment from which to extract data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "7dbf9e3a",
      "metadata": {
        "id": "7dbf9e3a",
        "outputId": "f1ad3582-8451-447b-a2cf-25124bde60ae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Specified segment '82924339_0007' in directory: 'mimic4wdb/0.1.0/waves/p101/p10126957/82924339'\n"
          ]
        }
      ],
      "source": [
        "rel_segment_n = 1\n",
        "rel_segment_name = segment_names[rel_segment_n]\n",
        "rel_segment_dir = segment_dirs[rel_segment_n]\n",
        "print(f\"Specified segment '{rel_segment_name}' in directory: '{rel_segment_dir}'\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fda3cdf7",
      "metadata": {
        "id": "fda3cdf7"
      },
      "source": [
        "---\n",
        "## Extract one minute of data from this segment"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "86c108e0",
      "metadata": {
        "id": "86c108e0"
      },
      "source": [
        "- Specify the timings of the data to be extracted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "75fd6b99",
      "metadata": {
        "id": "75fd6b99"
      },
      "outputs": [],
      "source": [
        "# time since the start of the segment at which to begin extracting data\n",
        "start_seconds = 20\n",
        "n_seconds_to_load = 60"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bb71616e",
      "metadata": {
        "id": "bb71616e"
      },
      "source": [
        "- Find out the sampling frequency of the waveform data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "ac21feb2",
      "metadata": {
        "id": "ac21feb2",
        "outputId": "aaa38f29-084f-4d7a-e097-4a1c11ea4fa1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metadata loaded from segment: 82924339_0007\n"
          ]
        }
      ],
      "source": [
        "segment_metadata = wfdb.rdheader(record_name=rel_segment_name,\n",
        "                                 pn_dir=rel_segment_dir)\n",
        "\n",
        "print(f\"Metadata loaded from segment: {rel_segment_name}\")\n",
        "fs = round(segment_metadata.fs)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cd93e67f",
      "metadata": {
        "id": "cd93e67f"
      },
      "source": [
        "- Extract the specified data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "8626ebac",
      "metadata": {
        "id": "8626ebac",
        "outputId": "8c398edc-75c8-412c-921f-54403c84a9aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60 seconds of data extracted from segment 82924339_0007\n"
          ]
        }
      ],
      "source": [
        "sampfrom = fs * start_seconds\n",
        "sampto = fs * (start_seconds + n_seconds_to_load)\n",
        "\n",
        "segment_data = wfdb.rdrecord(record_name=rel_segment_name,\n",
        "                             sampfrom=sampfrom,\n",
        "                             sampto=sampto,\n",
        "                             pn_dir=rel_segment_dir)\n",
        "\n",
        "print(f\"{n_seconds_to_load} seconds of data extracted from segment {rel_segment_name}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "20a17e39",
      "metadata": {
        "id": "20a17e39"
      },
      "source": [
        "---\n",
        "## Plot the extracted signals"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2ff2715c",
      "metadata": {
        "id": "2ff2715c"
      },
      "source": [
        "- Plot the extracted signals using the [plot_wfdb](https://wfdb.readthedocs.io/en/latest/plot.html#wfdb.plot.plot_wfdb) function from the WFDB Toolbox."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "62850216",
      "metadata": {
        "id": "62850216",
        "outputId": "0df6d129-9f8d-45ed-f572-753d876998e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "title_text = f\"Segment {rel_segment_name}\"\n",
        "wfdb.plot_wfdb(record=segment_data,\n",
        "               title=title_text,\n",
        "               time_units='seconds') "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e4278e0",
      "metadata": {
        "id": "8e4278e0"
      },
      "source": [
        "- Extract the PPG signal to loook at it more closely"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "5ddf9a02",
      "metadata": {
        "id": "5ddf9a02",
        "outputId": "8e09b85c-cf82-49a0-dbc9-f0c752936aae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracted the PPG signal from column 4 of the matrix of waveform data.\n"
          ]
        }
      ],
      "source": [
        "for sig_no in range(0, len(segment_data.sig_name)):\n",
        "    if \"Pleth\" in segment_data.sig_name[sig_no]:\n",
        "        break\n",
        "\n",
        "ppg = segment_data.p_signal[:, sig_no]\n",
        "fs = segment_data.fs\n",
        "print(f\"Extracted the PPG signal from column {sig_no} of the matrix of waveform data.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "68031e54",
      "metadata": {
        "id": "68031e54"
      },
      "source": [
        "<div class=\"alert alert-block alert-warning\"><p><b>Note:</b> the name given to PPG signals in the database is 'Pleth'.</p></div>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9b576fa9",
      "metadata": {
        "id": "9b576fa9"
      },
      "source": [
        "- Plot to look at the shape of the PPG pulse wave more closely"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "23d6edb2",
      "metadata": {
        "id": "23d6edb2",
        "outputId": "d9f180a8-1ab3-42d3-9d59-5279020674f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50.0, 55.0)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "t = np.arange(0, (len(ppg) / fs), 1.0 / fs)\n",
        "plt.plot(t, ppg, color = 'black', label='PPG')\n",
        "plt.xlim([50, 55])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eecf9314",
      "metadata": {
        "id": "eecf9314"
      },
      "source": [
        "---\n",
        "## Compare this to pulse waves from the literature"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ccd99c9",
      "metadata": {
        "id": "9ccd99c9"
      },
      "source": [
        "- Compare the pulse waves above to the different shapes of pulse waves shown here:"
      ]
    },
    {
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      "source": [
        "![PPG pulse waves](https://upload.wikimedia.org/wikipedia/commons/e/ed/Classes_of_photoplethysmogram_%28PPG%29_pulse_wave_shape.svg)"
      ]
    },
    {
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      "source": [
        "Source: _Charlton PH et al., 'Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet', https://doi.org/10.1152/ajpheart.00392.2021 (CC BY 4.0)_"
      ]
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        "These pulse waves are the typical shapes for young (class 1) to old (class 4) subjects."
      ]
    },
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      "source": [
        "<div class=\"alert alert-block alert-info\"><p><b>Question:</b> How do these pulse waves compare to those extracted from the MIMIC Database? Which is most similar?</p></div>"
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